• Active Portfolio management using systematic liquidity provisioning and yield farming strategies. • Generated a consistent profit with low level of speculation. Performed code audits for security and other analysis like effective yield, impermanent loss estimation etc.• Performed technical due diligence for several early-stage projects spanning various ecosystems like Polkadot, Arweave, Binance smart chain, Flow etc. for venture investments.• Developed components for tech stack including exchange wrapper libraries, dex arbitrage bots, on chain monitors etc. (details classified)

> Implemented core infrastructure for management of large trade datasets using Apache Spark for scalable solutions in Python and Java. Improved the speed by 5x over core SQL implementation for several data sources.> Developed real time trade analytics dashboard using Spark Streaming. It was successfully integrated to provide client solutions.

> Resolved Network Performance issues for improving IMS Latching time (VoLTE) between different Android Operating Systems by fixing bugs and implementing a new event at Telephony Framework layer. Rewarded with Star Performer award for improving overall time by 3 sec.> Implemented API for fetching of Mobile Number from the Network for a requirement in Netherlands. Co-worked with members from different teams to develop, test and integrate the new code to several devices successfully.> Member of eSIM Taskforce. Gained an in-depth understanding of eSIM architecture specifications (GSMA) and had technical discussions with HQ counterparts regarding Phase 2 implementation for Samsung Galaxy Watch and provided detailed documentations.> Incorporated Samsung specific changes by cherry-picking over Android 9 Pie (Go Edition) Google binary for the OS upgrade of Samsung Galaxy J2 device. The system upgrade was then sent to all J2 devices over the air in Southwest Asia region.

> Developed a Merchant Categorization model for graphical representation of different expenses using Google search API and Logistic Regression in Scala. Achieved an accuracy of 92% in batches tested.> Developed a Recommendation Engine using User-User Collaborative Filtering Algorithm for offer recommendation to the users with the RecDB framework API in Postgres and Scala.

Documentation and Implementation of various algorithms,concepts in Python likehttps://www.geeksforgeeks.org/push-relabel-algorithm-set-1-introduction-and-illustration/https://www.geeksforgeeks.org/push-relabel-algorithm-set-2-implementation/https://www.geeksforgeeks.org/how-to-use-oesis/